Introduction to ggplot2

class: center, middle, inverse, title-slide

# Introduction to ggplot2
### Antoine & Nicolas
### cynkra GmbH
### February 24th, 2022

---

# Exercises!

Let's wrap everything up ! Analyze gapminder data

```r
install.packages("gapminder")
library(gapminder)
gapminder
```

```
## # A tibble: 1,704 × 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## # … with 1,700 more rows
```

---

# Video introduction to the dataset

---

# Can we reproduce this ?

![](data:image/png;base64,#gapminder.png)

---

# Report

* We will work directly in a Rmd report as shown in previous part
* We give some steps to follow and your mission is to arrange those into a nice report
* We encourage you to take notes directly in the report

---

# America's poorer countries

* Use `summary(gapminder)` to get an overview of the data

* Filter the data for the Americas in 2007

* Create the variable gdp, defined as the product of population size and gdp per person.

* select `country`, `lifeExp`, `gdp` and `gdpPercap`

* Keep the 5 countries with the lowest gdp

---

# America's poorer countries

* Use the pipe to combine these operations
* save the new data in a variable
* print it
* plot it

---

# America's poorer countries

```r
library(tidyverse)

rich_americas_2007 <-
  gapminder %>%
  filter(continent == "Americas", year == 2007) %>% 
  mutate(gdp = gdpPercap * pop) %>% 
  select(country, lifeExp, gdp, gdpPercap) %>% 
  arrange(gdp) %>% 
  slice_head(n = 5)

rich_americas_2007
```

```
## # A tibble: 5 × 4
##   country             lifeExp          gdp gdpPercap
##   <fct>                 <dbl>        <dbl>     <dbl>
## 1 Haiti                  60.9 10217297216.     1202.
## 2 Nicaragua              72.9 15603375235.     2749.
## 3 Trinidad and Tobago    69.8 19027934931.    18009.
## 4 Jamaica                72.6 20353013485.     7321.
## # … with 1 more row
```

---

# America's poorer countries

```r
library(tidyverse)

ggplot(rich_americas_2007, aes(gdpPercap, lifeExp, color = country)) +
  geom_point(size = 10) +
  scale_y_continuous(limits = c(0, max(rich_americas_2007$lifeExp)))
```

![](data:image/png;base64,#tidy_intro_day4_2_ggplot-ii_files/figure-html/unnamed-chunk-4-1.png)

---

# Comparing continents

* Do the same but using averages by continent
* Do the same but using averages weighted by population
* place both plots side by side using {patchwork} 
  * use `+ plot_layout(guides = 'collect')` to collect legends
  * use `+ plot_annotation()` to add a global title
)

---

# Comparing continents

```r
continents <- gapminder %>% 
  group_by(continent) %>% 
  summarize(
    lifeExp_avg = mean(lifeExp), 
    lifeExp_wavg = sum(pop*lifeExp) / sum(pop), 
    gdpPercap_avg = mean(gdpPercap), 
    gdpPercap_wavg = sum(pop*gdpPercap) / sum(pop), 
    pop = sum(pop),
    .groups = "drop")
```

---

# Comparing continents

```r
p1 <- ggplot(continents, aes(lifeExp_avg, gdpPercap_avg, size = pop, color = continent)) +
  geom_point() + labs(title = "simple average")

p2 <- ggplot(continents, aes(lifeExp_wavg, gdpPercap_wavg, size = pop, color = continent)) +
  geom_point() + labs(title = "weighted average")

library(patchwork)
p1 + p2 + plot_layout(guides = 'collect') + plot_annotation("Averages can't be trusted!")
```

![](data:image/png;base64,#tidy_intro_day4_2_ggplot-ii_files/figure-html/unnamed-chunk-6-1.png)

---

# Comparing continents

This looks like a chart that should be faceted doesn't it ?

We'll need to transform it in tidy form for that. We want one observation per row,
where one observation translates to one dot on the chart.

```r
continents
```

```
## # A tibble: 5 × 6
##   continent lifeExp_avg lifeExp_wavg gdpPercap_avg gdpPercap_wavg         pop
##   <fct>           <dbl>        <dbl>         <dbl>          <dbl>       <dbl>
## 1 Africa           48.9         50.6         2194.          2108.  6187585961
## 2 Americas         64.7         69.5         7136.         15477.  7351438499
## 3 Asia             60.1         61.1         7902.          2950. 30507333901
## 4 Europe           71.9         72.3        14469.         15693.  6181115304
## # … with 1 more row
```

---

# Comparing continents

This is not trivial! but will sometimes be useful

```r
continents_long <- 
  continents %>% 
  pivot_longer(
    cols = lifeExp_avg:gdpPercap_wavg,
    names_pattern = "(.*)_(.*)", 
    names_to = c("var", "type"), 
    values_to = "value")

continents_long
```

```
## # A tibble: 20 × 5
##   continent        pop var       type   value
##   <fct>          <dbl> <chr>     <chr>  <dbl>
## 1 Africa    6187585961 lifeExp   avg     48.9
## 2 Africa    6187585961 lifeExp   wavg    50.6
## 3 Africa    6187585961 gdpPercap avg   2194. 
## 4 Africa    6187585961 gdpPercap wavg  2108. 
## # … with 16 more rows
```

---

# Comparing continents

This is not over because we want lifeExp and gdpPercap in separate columns

```r
continents_tidy <- 
  continents_long %>% 
  pivot_wider(names_from = var, values_from = value)

continents_tidy
```

```
## # A tibble: 10 × 5
##   continent        pop type  lifeExp gdpPercap
##   <fct>          <dbl> <chr>   <dbl>     <dbl>
## 1 Africa    6187585961 avg      48.9     2194.
## 2 Africa    6187585961 wavg     50.6     2108.
## 3 Americas  7351438499 avg      64.7     7136.
## 4 Americas  7351438499 wavg     69.5    15477.
## # … with 6 more rows
```

---

# Comparing continents

Now that it's tidy we can take our previous code and tweak it slightly :

```r
ggplot(continents_tidy, aes(lifeExp, gdpPercap, size = pop, color = continent)) +
  geom_point() + labs(title = "Averages can't be trusted!") +
  facet_wrap(vars(type))
```

![](data:image/png;base64,#tidy_intro_day4_2_ggplot-ii_files/figure-html/unnamed-chunk-10-1.png)

---

# Reproducing the plot : stage 1

Start again from the raw data

* plot the 2007 data, with color mapped to `continent` and size mapped to `pop`
* Use `alpha` in `geom_point()` to handle overplotting better
* Use a logarithmic scale for x (use `scale_x_log10()`)
* Set the colors manually using `scale_color_manual()` check the example at the
  bottom of `?scale_color_manual` (use a named vector).

Use Europe orange, Asia red, Africa = blue, Americas yellow, Oceania green
(Oceania is absent from picture, where Middle East has its own category, so we're a bit different)
You can improve your colors using: https://r-charts.com/colors/

---

# Reproducing the plot : stage 2

* Set the labels using `labs()` and remove the legend with `legend(position = "none")`
* Zoom on a similar window using `coord_cartesian`
* Set specific breaks on the x axis using the `breaks` arg in `scale_x_log10()`
* Set specific breaks on the y axis using the `breaks` arg in `scale_y_continuous()`

---

# Reproducing the plot : stage 3

* Use a nice default theme among the `theme_*()` functions of ggplot and increase font size
* Use `axis.text = element_text(size =)` in `theme()` to increase the size of the axis text,
 do the same for `axis.title`
* Increase the size of the bubbles, using `scale_size(range=)`
* Use the `label` argument in `scale_x_log10()` to set labels in dollars

---

# Reproducing the plot : stage 4

* Build a dataset containing only the data for Congo ("Congo, Dem. Rep."), Ghana and South Africa
* Use `geom_label()` to print the label on top of the chart
* Use `geom_point()` to draw a big circle for these 3 countries, use 
`shape = 14`, it looks like a regular dot but it has a `fill` and a `color`, 
set both to constant values

---

# We could go further!

What do we miss ?

* labels inside the axes
  * use annotate("text", ...)
* Specific fonts and color
  * use `theme()`
* background image
  * ggpubr::background_image
  
Almost anything is possible, Google is your friend!